Deep Learning
Deep learning, a subfield of machine learning, focuses on training artificial neural networks with multiple layers to extract complex patterns from data. Current research emphasizes improving model robustness against noisy or adversarial inputs, exploring efficient architectures like Vision Transformers and convolutional LSTMs for various tasks (e.g., image classification, time series forecasting), and integrating physics-informed approaches for enhanced interpretability and reliability. These advancements are significantly impacting diverse fields, from automated industrial inspection and medical image analysis to improved weather forecasting and more efficient content moderation systems.
Papers
Predictive uncertainty estimation in deep learning for lung carcinoma classification in digital pathology under real dataset shifts
Abdur R. Fayjie, Jutika Borah, Florencia Carbone, Jan Tack, Patrick Vandewalle
Applying Deep Neural Networks to automate visual verification of manual bracket installations in aerospace
John Oyekan, Liam Quantrill, Christopher Turner, Ashutosh Tiwari
Perspectives: Comparison of Deep Learning Segmentation Models on Biophysical and Biomedical Data
J Shepard Bryan, Meyam Tavakoli, Steve Presse
Deep Learning: a Heuristic Three-stage Mechanism for Grid Searches to Optimize the Future Risk Prediction of Breast Cancer Metastasis Using EHR-based Clinical Data
Xia Jiang, Yijun Zhou, Chuhan Xu, Adam Brufsky, Alan Wells
Automated Retinal Image Analysis and Medical Report Generation through Deep Learning
Jia-Hong Huang
Sign language recognition based on deep learning and low-cost handcrafted descriptors
Alvaro Leandro Cavalcante Carneiro, Denis Henrique Pinheiro Salvadeo, Lucas de Brito Silva
Massive Dimensions Reduction and Hybridization with Meta-heuristics in Deep Learning
Rasa Khosrowshahli, Shahryar Rahnamayan, Beatrice Ombuki-Berman
Integration of Genetic Algorithms and Deep Learning for the Generation and Bioactivity Prediction of Novel Tyrosine Kinase Inhibitors
Ricardo Romero
A Survey of Deep Learning for Group-level Emotion Recognition
Xiaohua Huang, Jinke Xu, Wenming Zheng, Qirong Mao, Abhinav Dhall
Deep Learning for Speaker Identification: Architectural Insights from AB-1 Corpus Analysis and Performance Evaluation
Matthias Bartolo
Artificial Neural Network and Deep Learning: Fundamentals and Theory
M. M. Hammad
Finding Patterns in Ambiguity: Interpretable Stress Testing in the Decision~Boundary
Inês Gomes, Luís F. Teixeira, Jan N. van Rijn, Carlos Soares, André Restivo, Luís Cunha, Moisés Santos
Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization
Holger Boche, Vit Fojtik, Adalbert Fono, Gitta Kutyniok
From Diagnostic CT to DTI Tractography labels: Using Deep Learning for Corticospinal Tract Injury Assessment and Outcome Prediction in Intracerebral Haemorrhage
Olivia N Murray, Hamied Haroon, Paul Ryu, Hiren Patel, George Harston, Marieke Wermer, Wilmar Jolink, Daniel Hanley, Catharina Klijn, Ulrike Hammerbeck, Adrian Parry-Jones, Timothy Cootes
MetMamba: Regional Weather Forecasting with Spatial-Temporal Mamba Model
Haoyu Qin, Yungang Chen, Qianchuan Jiang, Pengchao Sun, Xiancai Ye, Chao Lin
Deep Learning in Medical Image Registration: Magic or Mirage?
Rohit Jena, Deeksha Sethi, Pratik Chaudhari, James C. Gee
Deep Learning with Data Privacy via Residual Perturbation
Wenqi Tao, Huaming Ling, Zuoqiang Shi, Bao Wang
A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation
Koushik Biswas, Ridal Pal, Shaswat Patel, Debesh Jha, Meghana Karri, Amit Reza, Gorkem Durak, Alpay Medetalibeyoglu, Matthew Antalek, Yury Velichko, Daniela Ladner, Amir Borhani, Ulas Bagci
SRTFD: Scalable Real-Time Fault Diagnosis through Online Continual Learning
Dandan Zhao, Karthick Sharma, Hongpeng Yin, Yuxin Qi, Shuhao Zhang